Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations260503
Missing cells1442
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory188.1 MiB
Average record size in memory757.3 B

Variable types

Numeric9
DateTime1
Text4
Categorical5

Alerts

ARREST_BORO is highly overall correlated with ARREST_PRECINCT and 2 other fieldsHigh correlation
ARREST_PRECINCT is highly overall correlated with ARREST_BOROHigh correlation
KY_CD is highly overall correlated with LAW_CAT_CDHigh correlation
LAW_CAT_CD is highly overall correlated with KY_CDHigh correlation
Latitude is highly overall correlated with Y_COORD_CDHigh correlation
Longitude is highly overall correlated with X_COORD_CDHigh correlation
X_COORD_CD is highly overall correlated with ARREST_BORO and 1 other fieldsHigh correlation
Y_COORD_CD is highly overall correlated with ARREST_BORO and 1 other fieldsHigh correlation
LAW_CAT_CD is highly imbalanced (58.1%) Imbalance
Latitude is highly skewed (γ1 = -139.370098) Skewed
Longitude is highly skewed (γ1 = 154.9083432) Skewed
ARREST_KEY has unique values Unique
JURISDICTION_CODE has 224017 (86.0%) zeros Zeros

Reproduction

Analysis started2025-02-14 03:19:36.314658
Analysis finished2025-02-14 03:20:11.705014
Duration35.39 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

ARREST_KEY
Real number (ℝ)

Unique 

Distinct260503
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8939828 × 108
Minimum2.7976351 × 108
Maximum2.9874848 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-02-14T03:20:11.843010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.7976351 × 108
5-th percentile2.8080923 × 108
Q12.8472986 × 108
median2.8945965 × 108
Q32.9410416 × 108
95-th percentile2.9778914 × 108
Maximum2.9874848 × 108
Range18984975
Interquartile range (IQR)9374306

Descriptive statistics

Standard deviation5439968.3
Coefficient of variation (CV)0.018797514
Kurtosis-1.1865378
Mean2.8939828 × 108
Median Absolute Deviation (MAD)4683767
Skewness-0.028753483
Sum7.5389121 × 1013
Variance2.9593255 × 1013
MonotonicityNot monotonic
2025-02-14T03:20:12.092768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
281369711 1
 
< 0.1%
298661797 1
 
< 0.1%
290501031 1
 
< 0.1%
290234642 1
 
< 0.1%
290099583 1
 
< 0.1%
290650670 1
 
< 0.1%
289740038 1
 
< 0.1%
297652616 1
 
< 0.1%
289459427 1
 
< 0.1%
297069918 1
 
< 0.1%
Other values (260493) 260493
> 99.9%
ValueCountFrequency (%)
279763507 1
< 0.1%
279763792 1
< 0.1%
279763800 1
< 0.1%
279764505 1
< 0.1%
279764507 1
< 0.1%
279764510 1
< 0.1%
279764511 1
< 0.1%
279764512 1
< 0.1%
279764513 1
< 0.1%
279764515 1
< 0.1%
ValueCountFrequency (%)
298748482 1
< 0.1%
298725483 1
< 0.1%
298711176 1
< 0.1%
298711173 1
< 0.1%
298711171 1
< 0.1%
298711170 1
< 0.1%
298710745 1
< 0.1%
298710741 1
< 0.1%
298710736 1
< 0.1%
298710721 1
< 0.1%
Distinct366
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Minimum2024-01-01 00:00:00
Maximum2024-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-14T03:20:12.290258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:12.493488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PD_CD
Real number (ℝ)

Distinct267
Distinct (%)0.1%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean431.64201
Minimum2
Maximum997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-02-14T03:20:12.712560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile101
Q1117
median397
Q3705
95-th percentile922
Maximum997
Range995
Interquartile range (IQR)588

Descriptive statistics

Standard deviation271.55787
Coefficient of variation (CV)0.62912754
Kurtosis-1.1382518
Mean431.64201
Median Absolute Deviation (MAD)283
Skewness0.3297609
Sum1.1244058 × 108
Variance73743.679
MonotonicityNot monotonic
2025-02-14T03:20:12.932425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 28202
 
10.8%
339 27107
 
10.4%
109 15612
 
6.0%
922 13254
 
5.1%
478 12265
 
4.7%
397 11966
 
4.6%
779 9983
 
3.8%
439 9739
 
3.7%
511 7755
 
3.0%
113 6499
 
2.5%
Other values (257) 118113
45.3%
ValueCountFrequency (%)
2 1
 
< 0.1%
12 2
 
< 0.1%
15 43
 
< 0.1%
16 169
 
0.1%
29 2
 
< 0.1%
30 1
 
< 0.1%
35 7
 
< 0.1%
49 1165
 
0.4%
100 1
 
< 0.1%
101 28202
10.8%
ValueCountFrequency (%)
997 2
 
< 0.1%
973 1
 
< 0.1%
972 7
 
< 0.1%
969 2282
0.9%
968 62
 
< 0.1%
965 1
 
< 0.1%
963 2
 
< 0.1%
947 1
 
< 0.1%
940 67
 
< 0.1%
939 2
 
< 0.1%
Distinct257
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.4 MiB
2025-02-14T03:20:13.311055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length54
Median length52
Mean length25.094571
Min length6

Characters and Unicode

Total characters6537211
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)< 0.1%

Sample

1st rowSEXUAL ABUSE
2nd rowSTRANGULATION 1ST
3rd rowSTRANGULATION 1ST
4th rowSTRANGULATION 1ST
5th rowJOSTLING
ValueCountFrequency (%)
assault 46929
 
6.5%
3 42183
 
5.9%
from 38588
 
5.4%
open 36846
 
5.1%
areas 36846
 
5.1%
larceny,petit 27107
 
3.8%
criminal 18791
 
2.6%
controlled 17987
 
2.5%
2,1,unclassified 15612
 
2.2%
traffic,unclassified 15536
 
2.2%
Other values (383) 422955
58.8%
2025-02-14T03:20:13.795191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 618697
 
9.5%
E 586679
 
9.0%
A 568469
 
8.7%
I 476387
 
7.3%
N 468740
 
7.2%
464544
 
7.1%
R 366535
 
5.6%
T 341412
 
5.2%
L 328059
 
5.0%
C 313321
 
4.8%
Other values (32) 2004368
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6537211
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 618697
 
9.5%
E 586679
 
9.0%
A 568469
 
8.7%
I 476387
 
7.3%
N 468740
 
7.2%
464544
 
7.1%
R 366535
 
5.6%
T 341412
 
5.2%
L 328059
 
5.0%
C 313321
 
4.8%
Other values (32) 2004368
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6537211
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 618697
 
9.5%
E 586679
 
9.0%
A 568469
 
8.7%
I 476387
 
7.3%
N 468740
 
7.2%
464544
 
7.1%
R 366535
 
5.6%
T 341412
 
5.2%
L 328059
 
5.0%
C 313321
 
4.8%
Other values (32) 2004368
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6537211
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 618697
 
9.5%
E 586679
 
9.0%
A 568469
 
8.7%
I 476387
 
7.3%
N 468740
 
7.2%
464544
 
7.1%
R 366535
 
5.6%
T 341412
 
5.2%
L 328059
 
5.0%
C 313321
 
4.8%
Other values (32) 2004368
30.7%

KY_CD
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)< 0.1%
Missing32
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean252.54496
Minimum101
Maximum995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-02-14T03:20:13.941047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile105
Q1114
median341
Q3344
95-th percentile359
Maximum995
Range894
Interquartile range (IQR)230

Descriptive statistics

Standard deviation144.94242
Coefficient of variation (CV)0.5739272
Kurtosis5.1811136
Mean252.54496
Median Absolute Deviation (MAD)106
Skewness1.49722
Sum65780637
Variance21008.305
MonotonicityNot monotonic
2025-02-14T03:20:14.151488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
344 38238
14.7%
341 27107
 
10.4%
106 22606
 
8.7%
126 16360
 
6.3%
348 13783
 
5.3%
343 12621
 
4.8%
105 12020
 
4.6%
109 11804
 
4.5%
117 10424
 
4.0%
359 8712
 
3.3%
Other values (60) 86796
33.3%
ValueCountFrequency (%)
101 1601
 
0.6%
102 8
 
< 0.1%
103 60
 
< 0.1%
104 792
 
0.3%
105 12020
4.6%
106 22606
8.7%
107 6450
 
2.5%
109 11804
4.5%
110 2086
 
0.8%
111 2427
 
0.9%
ValueCountFrequency (%)
995 1390
0.5%
882 2
 
< 0.1%
881 2352
0.9%
880 78
 
< 0.1%
685 2
 
< 0.1%
678 181
 
0.1%
677 2192
0.8%
676 1
 
< 0.1%
675 139
 
0.1%
672 2
 
< 0.1%
Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.1 MiB
2025-02-14T03:20:14.429236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length28
Mean length20.019036
Min length4

Characters and Unicode

Total characters5215019
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowSEX CRIMES
2nd rowFELONY ASSAULT
3rd rowFELONY ASSAULT
4th rowFELONY ASSAULT
5th rowJOSTLING
ValueCountFrequency (%)
offenses 67295
 
8.3%
related 63764
 
7.9%
assault 60844
 
7.5%
59138
 
7.3%
larceny 40997
 
5.1%
3 38250
 
4.7%
dangerous 29299
 
3.6%
petit 27107
 
3.4%
felony 22606
 
2.8%
other 20756
 
2.6%
Other values (102) 376376
46.7%
2025-02-14T03:20:14.840714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 593170
11.4%
545929
10.5%
A 466154
 
8.9%
S 456542
 
8.8%
L 339538
 
6.5%
N 316131
 
6.1%
R 314901
 
6.0%
T 313796
 
6.0%
O 266257
 
5.1%
F 256252
 
4.9%
Other values (30) 1346349
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5215019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 593170
11.4%
545929
10.5%
A 466154
 
8.9%
S 456542
 
8.8%
L 339538
 
6.5%
N 316131
 
6.1%
R 314901
 
6.0%
T 313796
 
6.0%
O 266257
 
5.1%
F 256252
 
4.9%
Other values (30) 1346349
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5215019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 593170
11.4%
545929
10.5%
A 466154
 
8.9%
S 456542
 
8.8%
L 339538
 
6.5%
N 316131
 
6.1%
R 314901
 
6.0%
T 313796
 
6.0%
O 266257
 
5.1%
F 256252
 
4.9%
Other values (30) 1346349
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5215019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 593170
11.4%
545929
10.5%
A 466154
 
8.9%
S 456542
 
8.8%
L 339538
 
6.5%
N 316131
 
6.1%
R 314901
 
6.0%
T 313796
 
6.0%
O 266257
 
5.1%
F 256252
 
4.9%
Other values (30) 1346349
25.8%
Distinct1151
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
2025-02-14T03:20:15.070333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9998388
Min length6

Characters and Unicode

Total characters2604988
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique268 ?
Unique (%)0.1%

Sample

1st rowPL 1306501
2nd rowPL 1211200
3rd rowPL 1211200
4th rowPL 1211200
5th rowPL 1652501
ValueCountFrequency (%)
pl 235298
47.4%
1200001 27625
 
5.6%
1552500 27107
 
5.5%
1651503 11915
 
2.4%
vtl0511001 8786
 
1.8%
215510b 8462
 
1.7%
2200300 7755
 
1.6%
1200502 7684
 
1.5%
1553001 6231
 
1.3%
1201401 5372
 
1.1%
Other values (1146) 150255
30.3%
2025-02-14T03:20:15.510635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 667104
25.6%
1 410966
15.8%
5 276271
10.6%
L 257787
 
9.9%
P 236061
 
9.1%
235987
 
9.1%
2 232882
 
8.9%
3 61257
 
2.4%
6 59723
 
2.3%
4 43994
 
1.7%
Other values (31) 122956
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2604988
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 667104
25.6%
1 410966
15.8%
5 276271
10.6%
L 257787
 
9.9%
P 236061
 
9.1%
235987
 
9.1%
2 232882
 
8.9%
3 61257
 
2.4%
6 59723
 
2.3%
4 43994
 
1.7%
Other values (31) 122956
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2604988
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 667104
25.6%
1 410966
15.8%
5 276271
10.6%
L 257787
 
9.9%
P 236061
 
9.1%
235987
 
9.1%
2 232882
 
8.9%
3 61257
 
2.4%
6 59723
 
2.3%
4 43994
 
1.7%
Other values (31) 122956
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2604988
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 667104
25.6%
1 410966
15.8%
5 276271
10.6%
L 257787
 
9.9%
P 236061
 
9.1%
235987
 
9.1%
2 232882
 
8.9%
3 61257
 
2.4%
6 59723
 
2.3%
4 43994
 
1.7%
Other values (31) 122956
 
4.7%

LAW_CAT_CD
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing1390
Missing (%)0.5%
Memory size14.4 MiB
M
146772 
F
109140 
V
 
2233
9
 
734
I
 
226

Length

Max length6
Median length1
Mean length1.0001544
Min length1

Characters and Unicode

Total characters259153
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M 146772
56.3%
F 109140
41.9%
V 2233
 
0.9%
9 734
 
0.3%
I 226
 
0.1%
(null) 8
 
< 0.1%
(Missing) 1390
 
0.5%

Length

2025-02-14T03:20:15.784069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T03:20:15.963703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m 146772
56.6%
f 109140
42.1%
v 2233
 
0.9%
9 734
 
0.3%
i 226
 
0.1%
null 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M 146772
56.6%
F 109140
42.1%
V 2233
 
0.9%
9 734
 
0.3%
I 226
 
0.1%
l 16
 
< 0.1%
( 8
 
< 0.1%
n 8
 
< 0.1%
u 8
 
< 0.1%
) 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 259153
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 146772
56.6%
F 109140
42.1%
V 2233
 
0.9%
9 734
 
0.3%
I 226
 
0.1%
l 16
 
< 0.1%
( 8
 
< 0.1%
n 8
 
< 0.1%
u 8
 
< 0.1%
) 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 259153
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 146772
56.6%
F 109140
42.1%
V 2233
 
0.9%
9 734
 
0.3%
I 226
 
0.1%
l 16
 
< 0.1%
( 8
 
< 0.1%
n 8
 
< 0.1%
u 8
 
< 0.1%
) 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 259153
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 146772
56.6%
F 109140
42.1%
V 2233
 
0.9%
9 734
 
0.3%
I 226
 
0.1%
l 16
 
< 0.1%
( 8
 
< 0.1%
n 8
 
< 0.1%
u 8
 
< 0.1%
) 8
 
< 0.1%

ARREST_BORO
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.4 MiB
K
72325 
M
61969 
B
58521 
Q
56633 
S
11055 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260503
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowB
3rd rowM
4th rowK
5th rowM

Common Values

ValueCountFrequency (%)
K 72325
27.8%
M 61969
23.8%
B 58521
22.5%
Q 56633
21.7%
S 11055
 
4.2%

Length

2025-02-14T03:20:16.124917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T03:20:16.239445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
k 72325
27.8%
m 61969
23.8%
b 58521
22.5%
q 56633
21.7%
s 11055
 
4.2%

Most occurring characters

ValueCountFrequency (%)
K 72325
27.8%
M 61969
23.8%
B 58521
22.5%
Q 56633
21.7%
S 11055
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 260503
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
K 72325
27.8%
M 61969
23.8%
B 58521
22.5%
Q 56633
21.7%
S 11055
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 260503
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
K 72325
27.8%
M 61969
23.8%
B 58521
22.5%
Q 56633
21.7%
S 11055
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 260503
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
K 72325
27.8%
M 61969
23.8%
B 58521
22.5%
Q 56633
21.7%
S 11055
 
4.2%

ARREST_PRECINCT
Real number (ℝ)

High correlation 

Distinct79
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.410936
Minimum1
Maximum483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-02-14T03:20:16.712408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q140
median63
Q3101
95-th percentile115
Maximum483
Range482
Interquartile range (IQR)61

Descriptive statistics

Standard deviation34.955962
Coefficient of variation (CV)0.55126078
Kurtosis-0.9341508
Mean63.410936
Median Absolute Deviation (MAD)29
Skewness0.067836522
Sum16518739
Variance1221.9193
MonotonicityNot monotonic
2025-02-14T03:20:16.932166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 9887
 
3.8%
75 8675
 
3.3%
40 8389
 
3.2%
103 7983
 
3.1%
44 7690
 
3.0%
46 6605
 
2.5%
110 6440
 
2.5%
73 5727
 
2.2%
120 5535
 
2.1%
18 5487
 
2.1%
Other values (69) 188085
72.2%
ValueCountFrequency (%)
1 3419
 
1.3%
5 3640
 
1.4%
6 2285
 
0.9%
7 2351
 
0.9%
9 1813
 
0.7%
10 1961
 
0.8%
13 3804
 
1.5%
14 9887
3.8%
17 1168
 
0.4%
18 5487
2.1%
ValueCountFrequency (%)
483 3
 
< 0.1%
123 1052
 
0.4%
122 1625
 
0.6%
121 2843
1.1%
120 5535
2.1%
116 111
 
< 0.1%
115 5384
2.1%
114 4173
1.6%
113 5201
2.0%
112 1891
 
0.7%

JURISDICTION_CODE
Real number (ℝ)

Zeros 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90454237
Minimum0
Maximum97
Zeros224017
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-02-14T03:20:17.099423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum97
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.8823177
Coefficient of variation (CV)7.6086185
Kurtosis130.05739
Mean0.90454237
Median Absolute Deviation (MAD)0
Skewness11.102277
Sum235636
Variance47.366298
MonotonicityNot monotonic
2025-02-14T03:20:17.285375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 224017
86.0%
1 19880
 
7.6%
2 9920
 
3.8%
3 2119
 
0.8%
17 2012
 
0.8%
72 561
 
0.2%
97 473
 
0.2%
73 397
 
0.2%
11 263
 
0.1%
51 202
 
0.1%
Other values (15) 659
 
0.3%
ValueCountFrequency (%)
0 224017
86.0%
1 19880
 
7.6%
2 9920
 
3.8%
3 2119
 
0.8%
4 81
 
< 0.1%
7 122
 
< 0.1%
11 263
 
0.1%
12 10
 
< 0.1%
13 10
 
< 0.1%
14 107
 
< 0.1%
ValueCountFrequency (%)
97 473
0.2%
88 17
 
< 0.1%
87 112
 
< 0.1%
85 5
 
< 0.1%
79 6
 
< 0.1%
76 1
 
< 0.1%
74 2
 
< 0.1%
73 397
0.2%
72 561
0.2%
71 106
 
< 0.1%

AGE_GROUP
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.4 MiB
25-44
152034 
45-64
51121 
18-24
43174 
<18
 
9525
65+
 
4649

Length

Max length5
Median length5
Mean length4.8911798
Min length3

Characters and Unicode

Total characters1274167
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25-44
2nd row25-44
3rd row25-44
4th row25-44
5th row18-24

Common Values

ValueCountFrequency (%)
25-44 152034
58.4%
45-64 51121
 
19.6%
18-24 43174
 
16.6%
<18 9525
 
3.7%
65+ 4649
 
1.8%

Length

2025-02-14T03:20:17.450834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T03:20:17.580963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
25-44 152034
58.4%
45-64 51121
 
19.6%
18-24 43174
 
16.6%
18 9525
 
3.7%
65 4649
 
1.8%

Most occurring characters

ValueCountFrequency (%)
4 449484
35.3%
- 246329
19.3%
5 207804
16.3%
2 195208
15.3%
6 55770
 
4.4%
1 52699
 
4.1%
8 52699
 
4.1%
< 9525
 
0.7%
+ 4649
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1274167
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 449484
35.3%
- 246329
19.3%
5 207804
16.3%
2 195208
15.3%
6 55770
 
4.4%
1 52699
 
4.1%
8 52699
 
4.1%
< 9525
 
0.7%
+ 4649
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1274167
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 449484
35.3%
- 246329
19.3%
5 207804
16.3%
2 195208
15.3%
6 55770
 
4.4%
1 52699
 
4.1%
8 52699
 
4.1%
< 9525
 
0.7%
+ 4649
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1274167
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 449484
35.3%
- 246329
19.3%
5 207804
16.3%
2 195208
15.3%
6 55770
 
4.4%
1 52699
 
4.1%
8 52699
 
4.1%
< 9525
 
0.7%
+ 4649
 
0.4%

PERP_SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.4 MiB
M
213587 
F
46916 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260503
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 213587
82.0%
F 46916
 
18.0%

Length

2025-02-14T03:20:17.723131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T03:20:17.811525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m 213587
82.0%
f 46916
 
18.0%

Most occurring characters

ValueCountFrequency (%)
M 213587
82.0%
F 46916
 
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 260503
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 213587
82.0%
F 46916
 
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 260503
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 213587
82.0%
F 46916
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 260503
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 213587
82.0%
F 46916
 
18.0%

PERP_RACE
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
BLACK
122049 
WHITE HISPANIC
69131 
BLACK HISPANIC
26549 
WHITE
26161 
ASIAN / PACIFIC ISLANDER
14838 
Other values (2)
 
1775

Length

Max length30
Median length5
Mean length9.4737642
Min length5

Characters and Unicode

Total characters2467944
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBLACK
2nd rowBLACK
3rd rowBLACK
4th rowBLACK
5th rowWHITE

Common Values

ValueCountFrequency (%)
BLACK 122049
46.9%
WHITE HISPANIC 69131
26.5%
BLACK HISPANIC 26549
 
10.2%
WHITE 26161
 
10.0%
ASIAN / PACIFIC ISLANDER 14838
 
5.7%
UNKNOWN 956
 
0.4%
AMERICAN INDIAN/ALASKAN NATIVE 819
 
0.3%

Length

2025-02-14T03:20:17.945742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T03:20:18.078421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
black 148598
36.9%
hispanic 95680
23.8%
white 95292
23.7%
asian 14838
 
3.7%
14838
 
3.7%
pacific 14838
 
3.7%
islander 14838
 
3.7%
unknown 956
 
0.2%
american 819
 
0.2%
indian/alaskan 819
 
0.2%

Most occurring characters

ValueCountFrequency (%)
I 349280
14.2%
A 309363
12.5%
C 274773
11.1%
H 190972
 
7.7%
L 164255
 
6.7%
K 150373
 
6.1%
B 148598
 
6.0%
141832
 
5.7%
N 132319
 
5.4%
S 126175
 
5.1%
Other values (12) 480004
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2467944
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 349280
14.2%
A 309363
12.5%
C 274773
11.1%
H 190972
 
7.7%
L 164255
 
6.7%
K 150373
 
6.1%
B 148598
 
6.0%
141832
 
5.7%
N 132319
 
5.4%
S 126175
 
5.1%
Other values (12) 480004
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2467944
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 349280
14.2%
A 309363
12.5%
C 274773
11.1%
H 190972
 
7.7%
L 164255
 
6.7%
K 150373
 
6.1%
B 148598
 
6.0%
141832
 
5.7%
N 132319
 
5.4%
S 126175
 
5.1%
Other values (12) 480004
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2467944
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 349280
14.2%
A 309363
12.5%
C 274773
11.1%
H 190972
 
7.7%
L 164255
 
6.7%
K 150373
 
6.1%
B 148598
 
6.0%
141832
 
5.7%
N 132319
 
5.4%
S 126175
 
5.1%
Other values (12) 480004
19.4%

X_COORD_CD
Real number (ℝ)

High correlation 

Distinct31272
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1005551.9
Minimum0
Maximum1067220
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-02-14T03:20:18.288724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile979434
Q1990796
median1005257
Q31017771
95-th percentile1041920
Maximum1067220
Range1067220
Interquartile range (IQR)26975

Descriptive statistics

Standard deviation22036.796
Coefficient of variation (CV)0.021915125
Kurtosis166.99329
Mean1005551.9
Median Absolute Deviation (MAD)13631
Skewness-3.8353061
Sum2.6194929 × 1011
Variance4.8562036 × 108
MonotonicityNot monotonic
2025-02-14T03:20:18.492974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1017119 1692
 
0.6%
987220 1334
 
0.5%
1032084 1319
 
0.5%
962808 1318
 
0.5%
1005040 1298
 
0.5%
1020232 1266
 
0.5%
1041879 1235
 
0.5%
1011750 1224
 
0.5%
1026486 1219
 
0.5%
997897 1189
 
0.5%
Other values (31262) 247409
95.0%
ValueCountFrequency (%)
0 10
 
< 0.1%
913979 1
 
< 0.1%
914042 1
 
< 0.1%
914213 1
 
< 0.1%
914507 1
 
< 0.1%
914643 1
 
< 0.1%
914803 31
< 0.1%
914868 1
 
< 0.1%
914911 1
 
< 0.1%
914925 1
 
< 0.1%
ValueCountFrequency (%)
1067220 1
 
< 0.1%
1067185 5
< 0.1%
1066815 1
 
< 0.1%
1066674 1
 
< 0.1%
1066636 8
< 0.1%
1066615 2
 
< 0.1%
1066560 2
 
< 0.1%
1066523 1
 
< 0.1%
1066431 1
 
< 0.1%
1066424 1
 
< 0.1%

Y_COORD_CD
Real number (ℝ)

High correlation 

Distinct33189
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207816.82
Minimum0
Maximum271282
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-02-14T03:20:18.693144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile158969
Q1185644
median206961
Q3235593
95-th percentile253814
Maximum271282
Range271282
Interquartile range (IQR)49949

Descriptive statistics

Standard deviation29500.727
Coefficient of variation (CV)0.14195543
Kurtosis-0.77305126
Mean207816.82
Median Absolute Deviation (MAD)23850
Skewness-0.038671414
Sum5.4136906 × 1010
Variance8.7029288 × 108
MonotonicityNot monotonic
2025-02-14T03:20:18.896621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183909 1689
 
0.6%
212676 1334
 
0.5%
216954 1319
 
0.5%
174275 1318
 
0.5%
234533 1306
 
0.5%
210719 1266
 
0.5%
197083 1235
 
0.5%
250274 1224
 
0.5%
262591 1219
 
0.5%
175676 1176
 
0.5%
Other values (33179) 247417
95.0%
ValueCountFrequency (%)
0 10
< 0.1%
121508 1
 
< 0.1%
121900 1
 
< 0.1%
121929 1
 
< 0.1%
122258 1
 
< 0.1%
122533 1
 
< 0.1%
123092 1
 
< 0.1%
123163 2
 
< 0.1%
123321 1
 
< 0.1%
123357 1
 
< 0.1%
ValueCountFrequency (%)
271282 2
 
< 0.1%
271127 1
 
< 0.1%
270906 5
< 0.1%
270801 5
< 0.1%
270744 1
 
< 0.1%
270713 3
< 0.1%
270689 1
 
< 0.1%
270345 1
 
< 0.1%
270310 1
 
< 0.1%
270191 1
 
< 0.1%

Latitude
Real number (ℝ)

High correlation  Skewed 

Distinct42791
Distinct (%)16.4%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40.735495
Minimum0
Maximum40.911236
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-02-14T03:20:19.088260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.60274
Q140.67619
median40.734681
Q340.813303
95-th percentile40.863284
Maximum40.911236
Range40.911236
Interquartile range (IQR)0.13711317

Descriptive statistics

Standard deviation0.26504229
Coefficient of variation (CV)0.0065064212
Kurtosis21417.879
Mean40.735495
Median Absolute Deviation (MAD)0.065453734
Skewness-139.3701
Sum10611556
Variance0.070247415
MonotonicityNot monotonic
2025-02-14T03:20:19.303974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.671404 1584
 
0.6%
40.762037 1266
 
0.5%
40.810391 1260
 
0.5%
40.644996 1252
 
0.5%
40.750423 1243
 
0.5%
40.744981 1204
 
0.5%
40.853578 1179
 
0.5%
40.707439 1165
 
0.4%
40.887325 1164
 
0.4%
40.648859 1103
 
0.4%
Other values (42781) 248079
95.2%
ValueCountFrequency (%)
0 10
< 0.1%
40.49994 1
 
< 0.1%
40.501018 1
 
< 0.1%
40.501092 1
 
< 0.1%
40.501975 1
 
< 0.1%
40.502754 1
 
< 0.1%
40.504259 1
 
< 0.1%
40.50447061 2
 
< 0.1%
40.504915 1
 
< 0.1%
40.5050145 1
 
< 0.1%
ValueCountFrequency (%)
40.911236 2
 
< 0.1%
40.91081 1
 
< 0.1%
40.91020132 5
< 0.1%
40.909915 5
< 0.1%
40.909767 1
 
< 0.1%
40.90967503 3
< 0.1%
40.90960757 1
 
< 0.1%
40.908615 1
 
< 0.1%
40.908567 1
 
< 0.1%
40.908193 1
 
< 0.1%

Longitude
Real number (ℝ)

High correlation  Skewed 

Distinct42739
Distinct (%)16.4%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-73.920132
Minimum-74.252711
Maximum0
Zeros10
Zeros (%)< 0.1%
Negative260489
Negative (%)> 99.9%
Memory size2.0 MiB
2025-02-14T03:20:19.491584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.252711
5-th percentile-74.017299
Q1-73.976436
median-73.92417
Q3-73.879026
95-th percentile-73.791995
Maximum0
Range74.252711
Interquartile range (IQR)0.09741

Descriptive statistics

Standard deviation0.46430435
Coefficient of variation (CV)-0.0062811623
Kurtosis24659.983
Mean-73.920132
Median Absolute Deviation (MAD)0.049186534
Skewness154.90834
Sum-19256121
Variance0.21557853
MonotonicityNot monotonic
2025-02-14T03:20:19.683743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.881509 1584
 
0.6%
-73.827328 1266
 
0.5%
-73.924895 1260
 
0.5%
-74.077263 1252
 
0.5%
-73.98928 1244
 
0.5%
-73.870144 1204
 
0.5%
-73.900591 1179
 
0.5%
-73.792139 1165
 
0.4%
-73.847247 1164
 
0.4%
-73.95082 1103
 
0.4%
Other values (42729) 248078
95.2%
ValueCountFrequency (%)
-74.25271141 1
 
< 0.1%
-74.252487 1
 
< 0.1%
-74.251844 1
 
< 0.1%
-74.25081 1
 
< 0.1%
-74.250331 1
 
< 0.1%
-74.24975495 31
 
< 0.1%
-74.24952 1
 
< 0.1%
-74.24935766 1
 
< 0.1%
-74.249303 10
 
< 0.1%
-74.249302 192
0.1%
ValueCountFrequency (%)
0 10
< 0.1%
-73.70059685 1
 
< 0.1%
-73.700717 4
 
< 0.1%
-73.700719 1
 
< 0.1%
-73.702045 1
 
< 0.1%
-73.702535 1
 
< 0.1%
-73.702646 8
< 0.1%
-73.702756 2
 
< 0.1%
-73.702966 2
 
< 0.1%
-73.7030874 1
 
< 0.1%
Distinct44251
Distinct (%)17.0%
Missing4
Missing (%)< 0.1%
Memory size22.3 MiB
2025-02-14T03:20:20.046609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length28
Mean length32.947723
Min length11

Characters and Unicode

Total characters8582849
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18895 ?
Unique (%)7.3%

Sample

1st rowPOINT (-73.9410982410066 40.8009303727402)
2nd rowPOINT (-73.927554 40.833209)
3rd rowPOINT (-73.952863 40.778348)
4th rowPOINT (-73.905128 40.648698)
5th rowPOINT (-73.973717 40.763313)
ValueCountFrequency (%)
point 260499
33.3%
40.671404 1584
 
0.2%
73.881509 1584
 
0.2%
73.827328 1266
 
0.2%
40.762037 1266
 
0.2%
73.924895 1260
 
0.2%
40.810391 1260
 
0.2%
74.077263 1252
 
0.2%
40.644996 1252
 
0.2%
73.98928 1244
 
0.2%
Other values (85520) 509030
65.1%
2025-02-14T03:20:20.569797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 759713
 
8.9%
4 696425
 
8.1%
0 631201
 
7.4%
3 627318
 
7.3%
9 543342
 
6.3%
8 540252
 
6.3%
520998
 
6.1%
. 520978
 
6.1%
6 465600
 
5.4%
5 418798
 
4.9%
Other values (10) 2858224
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8582849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 759713
 
8.9%
4 696425
 
8.1%
0 631201
 
7.4%
3 627318
 
7.3%
9 543342
 
6.3%
8 540252
 
6.3%
520998
 
6.1%
. 520978
 
6.1%
6 465600
 
5.4%
5 418798
 
4.9%
Other values (10) 2858224
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8582849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 759713
 
8.9%
4 696425
 
8.1%
0 631201
 
7.4%
3 627318
 
7.3%
9 543342
 
6.3%
8 540252
 
6.3%
520998
 
6.1%
. 520978
 
6.1%
6 465600
 
5.4%
5 418798
 
4.9%
Other values (10) 2858224
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8582849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 759713
 
8.9%
4 696425
 
8.1%
0 631201
 
7.4%
3 627318
 
7.3%
9 543342
 
6.3%
8 540252
 
6.3%
520998
 
6.1%
. 520978
 
6.1%
6 465600
 
5.4%
5 418798
 
4.9%
Other values (10) 2858224
33.3%

Interactions

2025-02-14T03:20:06.308436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:50.261141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:52.203839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:54.134819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:56.708083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:58.803684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:00.709221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:02.547174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:04.341540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:06.506454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:50.485083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:52.399529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:54.337635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:57.006951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:59.002661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:00.932497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:02.775584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:04.522672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:06.835598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:50.694320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:52.580666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:54.628163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:57.293475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:59.181216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:01.121970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:02.964888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:04.744194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:07.074483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:50.901363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:52.955293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:54.923921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:57.561032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:59.569267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:01.316647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:03.159456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:04.925785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:07.280478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:51.118297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:53.169945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:55.193585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:57.746491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:59.779984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:01.552083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:03.340415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:05.106800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:07.488489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:51.343389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:53.372166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:55.475181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:57.934992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:59.957761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:01.770206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:03.533422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:05.288276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:07.747426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:51.553152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:53.556434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:55.773453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:58.132667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:00.143053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:01.957452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:03.773674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:05.465513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:08.036714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:51.778762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:53.755135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:56.067655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:58.332571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:00.331359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:02.153794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:03.974605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:05.653629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:08.329083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:51.976864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:53.931753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:56.372627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:19:58.565917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:00.517124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:02.335706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:04.154510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T03:20:05.837230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-14T03:20:20.729763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AGE_GROUPARREST_BOROARREST_KEYARREST_PRECINCTJURISDICTION_CODEKY_CDLAW_CAT_CDLatitudeLongitudePD_CDPERP_RACEPERP_SEXX_COORD_CDY_COORD_CD
AGE_GROUP1.0000.0250.0130.0130.0160.0680.0630.0040.0040.0860.0620.0230.0060.020
ARREST_BORO0.0251.0000.0100.7880.0510.0450.0490.0030.0030.0780.1650.0220.5380.552
ARREST_KEY0.0130.0101.000-0.0040.020-0.0020.0130.0020.000-0.0070.0110.0110.0000.002
ARREST_PRECINCT0.0130.788-0.0041.000-0.0700.0050.048-0.4720.3830.0260.1420.0130.384-0.472
JURISDICTION_CODE0.0160.0510.020-0.0701.0000.1230.0200.027-0.0180.0980.0280.016-0.0180.027
KY_CD0.0680.045-0.0020.0050.1231.0000.723-0.010-0.0020.1560.0380.074-0.002-0.010
LAW_CAT_CD0.0630.0490.0130.0480.0200.7231.0000.0340.0340.3930.0300.0510.0290.027
Latitude0.0040.0030.002-0.4720.027-0.0100.0341.0000.279-0.0470.0100.0000.2791.000
Longitude0.0040.0030.0000.383-0.018-0.0020.0340.2791.000-0.0080.0100.0001.0000.280
PD_CD0.0860.078-0.0070.0260.0980.1560.393-0.047-0.0081.0000.0440.167-0.008-0.047
PERP_RACE0.0620.1650.0110.1420.0280.0380.0300.0100.0100.0441.0000.0460.0840.145
PERP_SEX0.0230.0220.0110.0130.0160.0740.0510.0000.0000.1670.0461.0000.0220.019
X_COORD_CD0.0060.5380.0000.384-0.018-0.0020.0290.2791.000-0.0080.0840.0221.0000.279
Y_COORD_CD0.0200.5520.002-0.4720.027-0.0100.0271.0000.280-0.0470.1450.0190.2791.000

Missing values

2025-02-14T03:20:09.055063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-14T03:20:10.136933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-14T03:20:11.251799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ARREST_KEYARREST_DATEPD_CDPD_DESCKY_CDOFNS_DESCLAW_CODELAW_CAT_CDARREST_BOROARREST_PRECINCTJURISDICTION_CODEAGE_GROUPPERP_SEXPERP_RACEX_COORD_CDY_COORD_CDLatitudeLongitudeNew Georeferenced Column
028136971101/30/2024177.0SEXUAL ABUSE116.0SEX CRIMESPL 1306501FM25025-44MBLACK100055823108040.800930-73.941098POINT (-73.9410982410066 40.8009303727402)
128456140603/30/2024105.0STRANGULATION 1ST106.0FELONY ASSAULTPL 1211200FB44025-44MBLACK100429724284640.833209-73.927554POINT (-73.927554 40.833209)
228489601604/06/2024105.0STRANGULATION 1ST106.0FELONY ASSAULTPL 1211200FM19025-44MBLACK99730422285340.778348-73.952863POINT (-73.952863 40.778348)
328556901604/18/2024105.0STRANGULATION 1ST106.0FELONY ASSAULTPL 1211200FK69025-44MBLACK101057617562840.648698-73.905128POINT (-73.905128 40.648698)
428730895405/22/2024464.0JOSTLING230.0JOSTLINGPL 1652501MM18018-24MWHITE99153021737340.763313-73.973717POINT (-73.973717 40.763313)
528679333205/13/2024155.0RAPE 2104.0RAPEPL 1303001FQ112018-24MBLACK HISPANIC102540120258640.722641-73.851542POINT (-73.8515418216779 40.7226409964758)
627989260701/03/2024153.0RAPE 3104.0RAPEPL 1302503FQ113025-44MBLACK104631518708840.679981-73.776234POINT (-73.7762339071953 40.6799807384666)
728026390501/10/2024157.0RAPE 1104.0RAPEPL 1303501FB42025-44MBLACK100869023886240.822271-73.911698POINT (-73.911697780277 40.8222710411331)
828807231906/06/2024808.0TAX LAW125.0OTHER STATE LAWSTAX18140B3FM13045-64MBLACK98737321080540.745287-73.988729POINT (-73.98872939424497 40.7452870263689)
928840875306/12/2024105.0STRANGULATION 1ST106.0FELONY ASSAULTPL 1211200FB52045-64MBLACK101202625364940.862840-73.899580POINT (-73.89958 40.86284)
ARREST_KEYARREST_DATEPD_CDPD_DESCKY_CDOFNS_DESCLAW_CODELAW_CAT_CDARREST_BOROARREST_PRECINCTJURISDICTION_CODEAGE_GROUPPERP_SEXPERP_RACEX_COORD_CDY_COORD_CDLatitudeLongitudeNew Georeferenced Column
26049329840818112/23/2024106.0ASSAULT POLICE/PEACE OFFICER106.0FELONY ASSAULTPL 1200800FM19045-64FWHITE99696522150840.774655-73.954093POINT (-73.95409255249892 40.77465542447417)
26049429819388512/18/2024101.0ASSAULT 3344.0ASSAULT 3 & RELATED OFFENSESPL 1200001MQ102045-64MBLACK102472118777040.681978-73.854081POINT (-73.854081 40.681978)
26049529847250112/26/2024101.0ASSAULT 3344.0ASSAULT 3 & RELATED OFFENSESPL 1200001MQ1010<18MBLACK105364815896940.602748-73.750082POINT (-73.750082 40.602748)
26049629869045112/31/2024139.0MURDER,UNCLASSIFIED101.0MURDER & NON-NEGL. MANSLAUGHTEPL 1252501FB43018-24MBLACK HISPANIC102018323928240.823387-73.870170POINT (-73.87017 40.823387)
26049729829925312/20/2024439.0LARCENY,GRAND FROM OPEN AREAS, UNATTENDED109.0GRAND LARCENYPL 1553001FB52025-44FWHITE HISPANIC101099225361040.862735-73.903320POINT (-73.90332024389409 40.86273501411426)
26049829828797012/20/2024339.0LARCENY,PETIT FROM OPEN AREAS,341.0PETIT LARCENYPL 1552500MK90025-44MWHITE HISPANIC99804419886540.712514-73.950245POINT (-73.950245 40.712514)
26049929840128212/23/2024439.0LARCENY,GRAND FROM OPEN AREAS, UNATTENDED109.0GRAND LARCENYPL 1553001FM24045-64MWHITE HISPANIC99155822695640.789615-73.973609POINT (-73.9736085726657 40.78961486176856)
26050029862230712/30/2024922.0TRAFFIC,UNCLASSIFIED MISDEMEAN348.0VEHICLE AND TRAFFIC LAWSVTL05110MUMK67025-44MBLACK100342217850540.656611-73.930902POINT (-73.93090206546258 40.65661089034527)
26050129833581012/21/2024269.0MISCHIEF,CRIMINAL, UNCL 2ND121.0CRIMINAL MISCHIEF & RELATED OFPL 1450501FQ115025-44MWHITE HISPANIC102003521311140.751545-73.870843POINT (-73.87084320922126 40.75154455706598)
26050229854887112/27/2024681.0CHILD, ENDANGERING WELFARE233.0SEX CRIMESPL 2601001MB47025-44MBLACK102648026258440.887314-73.847272POINT (-73.8472717577564 40.8873136344706)